CN111209928A - Transformer equipment early warning and state prediction evaluation method and system - Google Patents

Transformer equipment early warning and state prediction evaluation method and system Download PDF

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CN111209928A
CN111209928A CN201911238336.0A CN201911238336A CN111209928A CN 111209928 A CN111209928 A CN 111209928A CN 201911238336 A CN201911238336 A CN 201911238336A CN 111209928 A CN111209928 A CN 111209928A
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纪陵
王永
杨小凡
檀庭方
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Nanjing SAC Automation Co Ltd
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Abstract

The invention discloses a transformer equipment early warning and state prediction evaluation method and a system thereof, which are characterized in that the transformer state is comprehensively sensed and classified with abnormal early warning by multi-dimensional sensing of key state quantities such as current, voltage, oil and gas quantity, temperature, meteorological environment and the like on three phases of a transformer, edge calculation is fully utilized, a support vector machine algorithm is applied, the transformer state is adaptively corrected according to the abnormal early warning type, the transformer overload capacity dynamic prediction and service life safety evaluation are realized, and an external system interface is provided to facilitate information to be sent to a cloud service center. The application shows that the method has the capability of autonomous sensing and analyzing the state of the transformer equipment, actively early warning the abnormal operation and providing effective technical support for improving the operation and inspection efficiency of the transformer.

Description

Transformer equipment early warning and state prediction evaluation method and system
Technical Field
The invention relates to a transformer equipment early warning and state prediction evaluation method and a transformer equipment early warning and state prediction evaluation system, and belongs to the technical field of power transmission and transformation equipment internet of things.
Background
The state network is ubiquitous on the Internet of things, and the strategy of 'three-type two-network and world-first-class' is accelerated to be implemented. Due to the fact that the scale of a power grid is continuously enlarged, the operation and inspection management requirements are continuously changed, the operation and inspection work total amount is continuously improved, and the conventional transformer substation online monitoring system scheme mainly aims at the health state of a transformer body and adopts the existing independent special devices to monitor one type of health characteristics from different angles. From the angle of monitoring and information integration, the transformer monitoring information application level of a conventional transformer substation is low, and operation and inspection personnel lack effective auxiliary means when working in the substation, cannot comprehensively master the operation state information of the transformer, and cannot make accurate diagnosis on the state of the transformer.
Disclosure of Invention
In order to solve the technical problems, the invention provides a transformer equipment early warning and state prediction evaluation method and a transformer equipment state prediction evaluation system, and solves the problem that the state of transformer equipment cannot be comprehensively mastered and accurately judged.
In order to achieve the purpose, the invention adopts the technical scheme that: a transformer equipment state early warning and state prediction evaluation method comprises the following steps:
acquiring data of multidimensional sensing of the transformer, and predicting an electric quantity and an oil-gas quantity early warning type according to the data of the multidimensional sensing;
calculating a current early warning evaluation index coefficient according to an early warning type sequence consisting of predicted electric quantity and oil-gas quantity early warning types;
and predicting and evaluating the state of the transformer according to the current early warning evaluation index coefficient.
Further, the data sensed in the multidimensional mode by the transformer comprise oil gas quantity, electric quantity, internal temperature of the transformer and ambient temperature of the transformer.
Further, the electric quantity early warning type is predicted, and the process is as follows:
according to the electric quantity data of the transformer, turn-to-turn short circuit early warning analysis is carried out, and the internal loss power of the transformer can be expressed as follows:
ΔP＝P1-P2；
wherein, Δ P is total transformer loss, P1For feeding active power to the transformer, P2Outputting power for the transformer; and calculating the total loss of the three phases according to the current and voltage values, and if the total loss of the three phases exceeds a threshold value, indicating that turn-to-turn short circuit occurs and early warning the turn-to-turn short circuit.
Further, the early warning type of the oil and gas amount is predicted, and the process is as follows:
and inputting the acquired transformer oil-gas quantity data according to a pre-established oil-gas quantity early warning model of the support vector machine SVM to obtain a predicted oil-gas quantity early warning type.
Further, the hydrocarbon volume data comprises: the early warning types of the contents of hydrogen, carbon monoxide, ethylene, acetylene, ethane and carbon dioxide include general overheating of 100-120 ℃, severe overheating of 120-140 ℃, partial discharge, spark discharge, arc discharge, overheating and arc discharge and normal operation.
Further, the formula of the early warning evaluation index coefficient is as follows:
Figure BDA0002305488360000021
Figure BDA0002305488360000022
wj∈(0,1)，j＝1，2，…，M
wherein: d is an early warning evaluation index coefficient, wjThe anomaly early warning type is weight value of j, DjAnd coding the early warning type j in the early warning type sequence.
Further, the transformer state is subjected to prediction evaluation according to the current early warning evaluation index coefficient, and the method comprises the following steps:
correcting a calculation formula of the hot spot temperature of the transformer winding:
Figure BDA0002305488360000023
wherein:
Figure BDA0002305488360000024
indicating the corrected transformer winding hot spot temperature thetahThe hot spot temperature is calculated in real time according to a transformer hot circuit model;
θh＝θa+Δθor[(1+RK2)/(1+R)]+H(Δθwr-Δθimr)K2
wherein: thetahIs the transformer winding hot spot temperature, thetaaFor cooling medium temperature, Δ θorTemperature rise of the top oil, Delta theta, measured for the temperature rise testwrAverage temperature rise, Δ θ, of the winding measured for the temperature rise testimrR is the ratio of the load loss and the load loss of the transformer, H is the hot spot coefficient, and K is the load coefficient of the transformer.
Further, the transformer state is subjected to prediction evaluation, and the prediction evaluation on the relative aging rate, the allowable operation time and the life loss time of the transformer is also carried out:
the transformer relative aging rate is expressed as:
Figure BDA0002305488360000031
wherein: v is the relative aging rate of the transformer,
Figure BDA0002305488360000032
the hot spot temperature of the transformer winding after correction.
With 24 hours as an operation period, the allowable operation time T of the transformer is as follows:
Figure BDA0002305488360000033
the loss of life L over a certain period is expressed as:
Figure BDA0002305488360000034
wherein: l is the loss of life, VnIs the relative aging rate, t, in the nth time intervalnThe time of the nth time interval is N, N is the ordinal number of each time interval, N is 1,2, …, and N is the total number of time intervals.
A transformer equipment state early warning and state prediction evaluation system comprises:
the early warning type prediction module for the electric quantity and the oil gas quantity is used for acquiring data of the multi-dimensional sensing of the transformer and predicting the early warning type of the electric quantity and the oil gas quantity according to the data of the multi-dimensional sensing;
the current early warning evaluation index coefficient calculation module is used for calculating a current early warning evaluation index coefficient according to an early warning type sequence consisting of the predicted electric quantity and the early warning type of the oil and gas quantity;
and the transformer state prediction and evaluation module is used for predicting and evaluating the transformer state according to the current early warning evaluation index coefficient.
Further, the transformer state is subjected to prediction evaluation according to the current early warning evaluation index coefficient, and the method comprises the following steps:
correcting a calculation formula of the hot spot temperature of the transformer winding:
Figure BDA0002305488360000035
wherein:
Figure BDA0002305488360000036
indicating the corrected transformer winding hot spot temperature thetahThe hot spot temperature is calculated in real time according to a transformer hot circuit model;
θh＝θa+Δθor[(1+RK2)/(1+R)]+H(Δθwr-Δθimr)K2
wherein: thetahIs the transformer winding hot spot temperature, thetaaFor cooling medium temperature, Δ θorTop oil as measured by temperature rise testTemperature rise, Delta thetawrAverage temperature rise, Δ θ, of the winding measured for the temperature rise testimrR is the ratio of the load loss and the load loss of the transformer, H is the hot spot coefficient, and K is the load coefficient of the transformer.
The invention achieves the following beneficial effects:
1. the prediction result is more accurate by the transformer abnormity early warning classification algorithm combining the electric quantity and the oil gas quantity;
2. the transformer equipment state evaluation prediction analysis capability is achieved, and effective technical support is provided for improving the transformer operation and inspection efficiency;
3. the transformer overload capacity dynamic prediction and service life safety assessment are realized by carrying out multi-dimensional perception on key state quantities such as current, voltage, oil-gas quantity, temperature and meteorological environment on three phases of the transformer, fully utilizing edge calculation, applying a support vector machine algorithm, carrying out comprehensive perception and abnormity early warning classification on the state of the transformer, adaptively correcting a calculation result of the hot point temperature of a winding of the transformer according to an abnormity early warning type, and providing an external system interface to facilitate information to be sent to a cloud service center. The application shows that the method has the capability of autonomous sensing and analyzing the state of the transformer equipment, actively early warning the abnormal operation and providing effective technical support for improving the operation and inspection efficiency of the transformer.
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FIG. 1 is a flow chart of a method in an embodiment.
Detailed Description
The invention is further described below with reference to the figures and examples. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
Example 1:
as shown in fig. 1, the embodiment provides a transformer device early warning and state prediction evaluation method, which includes the following steps:
step 1, collecting data of multidimensional sensing of a transformer, and predicting an electric quantity and an oil and gas quantity early warning type on a transformer substation edge server according to the data of the multidimensional sensing;
the data of transformer multidimension perception includes transformer oil gas volume, electric volume, transformer internal temperature, ambient temperature: multidimensional acquisition and sensing are carried out on the state of the transformer through the sensor of the Internet of things, electric quantities such as phase current, voltage and power factors of three sides of the transformer are acquired, and oil-gas quantity data of dissolved gas in transformer oil on-line monitoring, internal temperature of the transformer and ambient temperature are acquired.
Predicting the electric quantity early warning type, which comprises the following specific processes:
according to the electric quantity data of the transformer, turn-to-turn short circuit early warning analysis is carried out, and the internal loss power of the transformer can be expressed as follows:
ΔP＝P1-P2；
wherein, Δ P is total transformer loss, P1For feeding active power to the transformer, P2And outputting power for the transformer. And if the total loss exceeds a threshold value, indicating that turn-to-turn short circuit occurs, and early warning the turn-to-turn short circuit, wherein the turn-to-turn short circuit early warning type is an electric quantity early warning type.
Predicting the early warning type of oil and gas quantity, which comprises the following specific processes:
inputting collected transformer oil-gas quantity data according to a pre-established oil-gas quantity early warning model of a Support Vector Machine (SVM), and obtaining a predicted oil-gas quantity early warning type;
the oil and gas quantity early warning model establishment process of the support vector machine SVM comprises the following steps: and carrying out correlation analysis on the composition analysis of the dissolved gas in the transformer oil and the early warning type of the transformer. And training a model according to the historical transformer oil and gas quantity acquisition data to obtain algorithm parameters. The method is characterized in that the contents of hydrogen, carbon monoxide, ethylene, acetylene, ethane and carbon dioxide are used as input characteristic quantities, the output is an oil-gas quantity early warning type, and the oil-gas quantity early warning type comprises general overheating (100-120) DEG C, serious overheating (120-140) DEG C, partial discharge, spark discharge, arc discharge, overheating and arc discharge and normal operation.
Step 2, calculating a current early warning evaluation index coefficient according to an early warning type sequence consisting of the predicted electric quantity and oil and gas quantity early warning types;
because the influence degree of each early warning type on the state evaluation of the transformer is different, a weight value is calculated for each early warning type. If the number of the early warning types is M, the early warning type influence evaluation formula is expressed as follows:
wj∈(0,1)，j＝1，2，…，M
Figure BDA0002305488360000051
Figure BDA0002305488360000052
wherein: w is ajThe weight value with the abnormal early warning type j can be obtained by adopting a least square method according to historical data, D is an evaluation index coefficient of early warning analysis, and DjAnd coding in the early warning type sequence of the early warning type j. The electric quantity and the oil gas quantity early warning types are formed into a sequence (8 numbers in the sequence of the embodiment)
The character is that 0 indicates that no early warning occurs, 1 indicates that early warning occurs, and at most two 1 s exist in the sequence and respectively represent the early warning types of the electrical quantity and the oil-gas quantity); such as [1,1,0,0,0,0,0,0], indicating that turn-to-turn short circuits and general overheating have occurred;
and (4) calculating an evaluation index coefficient of the early warning analysis according to the early warning type obtained in the step (1).
Step 3, predicting the state of the transformer according to the current early warning evaluation index coefficient;
the predicted transformer state comprises predictive analysis of transformer winding hot spot temperature, relative aging rate, life loss time and allowable run time.
The calculation formula of the hot spot temperature of the transformer winding for forced oil circulation cooling is as follows:
θh＝θa+Δθor[(1+RK2)/(1+R)]+H(Δθwr-Δθimr)K2
wherein: thetahFor hot spot temperature of transformer windingDegree, thetaaIs the cooling medium temperature (i.e., ambient temperature), Δ θorTemperature rise of the top oil, Delta theta, measured for the temperature rise testwrAverage temperature rise, Δ θ, of the winding measured for the temperature rise testimrR is the ratio of the load loss and the load loss of the transformer, H is the hot spot coefficient, and K is the load coefficient of the transformer.
In the above formula, the calculation of the transformer hot spot temperature is an analysis result performed at the design operation temperature of the transformer, the temperature change of the transformer under the abnormal condition is not considered, the index coefficient is evaluated according to the step 2, the transformer winding hot spot temperature is corrected, and a more accurate result can be obtained, wherein the calculation formula is as follows:
Figure BDA0002305488360000061
wherein:
Figure BDA0002305488360000062
indicating the dynamic hot spot temperature theta of the corrected transformer windinghThe hot spot temperature is calculated in real time according to the transformer hot circuit model, and D is the current early warning evaluation index coefficient.
The aging or degradation model of the transformer insulation takes the insulation temperature as a control parameter, the relative aging rate takes the winding hot spot temperature as a reference, and the relative aging rate of the non-thermal modified paper can be taken as the transformer relative aging rate, which can be expressed as:
Figure BDA0002305488360000063
wherein: v is the relative aging rate of the transformer,
Figure BDA0002305488360000064
the hot spot temperature of the transformer winding after correction.
With 24 hours as an operation period, the allowed operation time of the transformer is as follows:
Figure BDA0002305488360000065
wherein T is the allowable operation time, and V is the relative aging rate of the transformer.
The loss of life L over a period of time can be expressed as:
Figure BDA0002305488360000071
wherein: l is the loss of life, VnIs the relative aging rate, t, in the nth time intervalnThe time of the nth time interval is N, N is the ordinal number of each time interval, N is 1,2, …, and N is the total number of time intervals.
Example 2:
a transformer equipment state early warning and state prediction evaluation system comprises:
the early warning type prediction module for the electric quantity and the oil gas quantity is used for acquiring data of the multi-dimensional sensing of the transformer and predicting the early warning type of the electric quantity and the oil gas quantity according to the data of the multi-dimensional sensing;
the current early warning evaluation index coefficient calculation module is used for calculating a current early warning evaluation index coefficient according to an early warning type sequence consisting of the predicted electric quantity and the early warning type of the oil and gas quantity;
and the transformer state prediction and evaluation module is used for predicting and evaluating the transformer state according to the current early warning evaluation index coefficient.
Further, the transformer state is subjected to prediction evaluation according to the current early warning evaluation index coefficient, and the method comprises the following steps:
correcting a calculation formula of the hot spot temperature of the transformer winding:
Figure BDA0002305488360000072
wherein:
Figure BDA0002305488360000073
indicating the corrected transformer winding hot spot temperature thetahThe hot spot temperature is calculated in real time according to a transformer hot circuit model;
θh＝θa+Δθor[(1+RK2)/(1+R)]+H(Δθwr-Δθimr)K2
wherein: thetahIs the transformer winding hot spot temperature, thetaaFor cooling medium temperature, Δ θorTemperature rise of the top oil, Delta theta, measured for the temperature rise testwrAverage temperature rise, Δ θ, of the winding measured for the temperature rise testimrR is the ratio of the load loss and the load loss of the transformer, H is the hot spot coefficient, and K is the load coefficient of the transformer.
In conclusion, the prediction result is more accurate by the transformer abnormity early warning classification algorithm combining the electric quantity and the oil-gas quantity; the transformer equipment state evaluation prediction analysis capability is achieved, and effective technical support is provided for improving the transformer operation and inspection efficiency.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.

Claims (10)

1. A transformer equipment state early warning and state prediction evaluation method is characterized by comprising the following steps: the method comprises the following steps:
acquiring data of multidimensional sensing of the transformer, and predicting an electric quantity and an oil-gas quantity early warning type according to the data of the multidimensional sensing;
calculating a current early warning evaluation index coefficient according to an early warning type sequence consisting of predicted electric quantity and oil-gas quantity early warning types;
and predicting and evaluating the state of the transformer according to the current early warning evaluation index coefficient.
2. The transformer equipment state early warning and state prediction evaluation method according to claim 1, characterized in that: the data of the transformer multi-dimensional sensing comprise the oil gas quantity, the electric quantity, the internal temperature of the transformer and the ambient temperature of the transformer.
3. The transformer equipment state early warning and state prediction evaluation method according to claim 1, characterized in that: predicting the electric quantity early warning type, the process is as follows:
according to the electric quantity data of the transformer, turn-to-turn short circuit early warning analysis is carried out, and the internal loss power of the transformer can be expressed as follows:
ΔP＝P1-P2；
wherein, Δ P is total transformer loss, P1For feeding active power to the transformer, P2Outputting power for the transformer; and calculating the total loss of the three phases according to the current and voltage values, and if the total loss of the three phases exceeds a threshold value, indicating that turn-to-turn short circuit occurs and early warning the turn-to-turn short circuit.
4. The transformer equipment state early warning and state prediction evaluation method according to claim 1, characterized in that: predicting the early warning type of oil and gas quantity, the process is as follows:
and inputting the acquired transformer oil-gas quantity data according to a pre-established oil-gas quantity early warning model of the support vector machine SVM to obtain a predicted oil-gas quantity early warning type.
5. The transformer equipment state early warning and state prediction evaluation method according to claim 4, characterized in that: the hydrocarbon volume data comprises: the early warning types of the contents of hydrogen, carbon monoxide, ethylene, acetylene, ethane and carbon dioxide include general overheating of 100-120 ℃, severe overheating of 120-140 ℃, partial discharge, spark discharge, arc discharge, overheating and arc discharge and normal operation.
6. The transformer equipment state early warning and state prediction evaluation method according to claim 1, characterized in that: the early warning evaluation index coefficient formula is as follows:
Figure FDA0002305488350000021
Figure FDA0002305488350000022
wj∈(0,1)，j＝1，2，…，M
wherein: d is an early warning evaluation index coefficient, wjThe anomaly early warning type is weight value of j, DjAnd coding the early warning type j in the early warning type sequence.
7. The transformer equipment state early warning and state prediction evaluation method according to claim 6, characterized in that: the transformer state is predicted and evaluated according to the current early warning evaluation index coefficient, and the method comprises the following steps:
correcting a calculation formula of the hot spot temperature of the transformer winding:
Figure FDA0002305488350000023
wherein:
Figure FDA0002305488350000024
indicating the corrected transformer winding hot spot temperature thetahThe hot spot temperature is calculated in real time according to a transformer hot circuit model;
θh＝θa+Δθor[(1+RK2)/(1+R)]+H(Δθwr-Δθimr)K2
wherein: thetahIs the transformer winding hot spot temperature, thetaaFor cooling medium temperature, Δ θorTemperature rise of the top oil, Delta theta, measured for the temperature rise testwrAverage temperature rise, Δ θ, of the winding measured for the temperature rise testimrR is the ratio of the load loss and the load loss of the transformer, H is the hot spot coefficient, and K is the load coefficient of the transformer.
8. The transformer equipment state early warning and state prediction evaluation method according to claim 7, characterized in that: the transformer state is subjected to prediction evaluation, and the method also comprises the following steps of performing prediction evaluation on the relative aging rate, the allowable operation time and the life loss time of the transformer:
the transformer relative aging rate is expressed as:
Figure FDA0002305488350000025
wherein: v is the relative aging rate of the transformer,
Figure FDA0002305488350000026
the hot spot temperature of the transformer winding after correction.
With 24 hours as an operation period, the allowable operation time T of the transformer is as follows:
Figure FDA0002305488350000031
the loss of life L over a certain period is expressed as:
Figure FDA0002305488350000032
wherein: l is the loss of life, VnIs the relative aging rate, t, in the nth time intervalnThe time of the nth time interval is N, N is the ordinal number of each time interval, N is 1,2, …, and N is the total number of time intervals.
9. The transformer equipment state early warning and state prediction evaluation system is characterized in that: the method comprises the following steps:
the early warning type prediction module for the electric quantity and the oil gas quantity is used for acquiring data of the multi-dimensional sensing of the transformer and predicting the early warning type of the electric quantity and the oil gas quantity according to the data of the multi-dimensional sensing;
the current early warning evaluation index coefficient calculation module is used for calculating a current early warning evaluation index coefficient according to an early warning type sequence consisting of the predicted electric quantity and the early warning type of the oil and gas quantity;
and the transformer state prediction and evaluation module is used for predicting and evaluating the transformer state according to the current early warning evaluation index coefficient.
10. The transformer equipment state early warning and state prediction evaluation system of claim 9, wherein: the transformer state is predicted and evaluated according to the current early warning evaluation index coefficient, and the method comprises the following steps:
correcting a calculation formula of the hot spot temperature of the transformer winding:
Figure FDA0002305488350000033
wherein:
Figure FDA0002305488350000034
indicating the corrected transformer winding hot spot temperature thetahThe hot spot temperature is calculated in real time according to a transformer hot circuit model;
θh＝θa+Δθor[(1+RK2)/(1+R)]+H(Δθwr-Δθimr)K2
wherein: thetahIs the transformer winding hot spot temperature, thetaaFor cooling medium temperature, Δ θorTemperature rise of the top oil, Delta theta, measured for the temperature rise testwrAverage temperature rise, Δ θ, of the winding measured for the temperature rise testimrR is the ratio of the load loss and the load loss of the transformer, H is the hot spot coefficient, and K is the load coefficient of the transformer.
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CN112162847A (en) * 2020-08-19 2021-01-01 中国电力科学研究院有限公司 Power transformer state sensing and evaluating system and method
CN114167179A (en) * 2021-12-03 2022-03-11 中国海洋大学 Method and system for evaluating health state of distribution transformer for low-voltage power supply of rail transit
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